Table of Content
I.	Project Summary
II.	Workflow Checklist
III.	NGS Sequencing Results
IV.	Complete Report Download
V.	Raw Sequence Data Download
VI.	Analysis - DADA2 Read Processing
	Sample Meta Info
	Read Count by Sample
VII.	Analysis - Read Taxonomy Assignment
	Taxonomy Barplots
VIII.	Analysis - Alpha Diversity
IX.	Analysis - Beta Diversity
X.	Analysis - Differential Abundance
	ANCOM Result
	LEfSe Result
XI.	Analysis - Heatmap Profile
XII.	Analysis - Network Association

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FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.41

Version History

The Forsyth Institute, Cambridge, MA, USA

May 02, 2022

Project ID: FOMC4083_4106_4511_4917

I. Project Summary

Project FOMC4083_4106_4511_4917 services include NGS sequencing of the V1V3 region of the 16S rRNA amplicons from the samples. First and foremost, please download this report, as well as the sequence raw data from the download links provided below. These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.

Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the DADA2 denosing algorithm and pipeline.

We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.

For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.

If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.

II. Workflow Checklist

☑	1.	Sample Received
☑	2.	Sample Quality Evaluated
☑	3.	Sample Prepared for Sequencing
☑	4.	Next-Gen Sequencing
☑	5.	Sequence Quality Check
☑	6.	Absolute Abundance
☑	7.	Report and Raw Sequence Data Available for Download
☑	8.	Bioinformatics Analysis - Reads Processing (DADA2 Quality Trimming, Denoising, Paired Reads Merging)
☑	9.	Bioinformatics Analysis - Reads Taxonomy Assignment
☑	10.	Bioinformatics Analysis - Alpha Diversity Analysis
☑	11.	Bioinformatics Analysis - Beta Diversity Analysis
☑	12.	Bioinformatics Analysis - Differential Abundance Analysis
☑	13.	Bioinformatics Analysis - Heatmap Profile
☑	14.	Bioinformatics Analysis - Network Association

III. NGS Sequencing

The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted Metagenomic Sequencing (Zymo Research, Irvine, CA).

DNA Extraction: If DNA extraction was performed, one of three different DNA extraction kits was used depending on the sample type and sample volume and were used according to the manufacturer’s instructions, unless otherwise stated. The kit used in this project is marked below:

☐	ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐	ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐	ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑	N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA). These primers were custom designed by Zymo Research to provide the best coverage of the 16S gene while maintaining high sensitivity. The primer sets used in this project are marked below:

☐	Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑	Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐	Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐	Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐	Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐	Other: NA
Additional Notes: NA

The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™ (Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).

Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo Research, Irvine, CA) was used as a positive control for each DNA extraction, if performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research, Irvine, CA) was used as a positive control for each targeted library preparation. Negative controls (i.e. blank extraction control, blank library preparation control) were included to assess the level of bioburden carried by the wet-lab process.

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit (600 cycles). The sequencing was performed with 10% PhiX spike-in.

IV. Complete Report Download

The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Complete report download link:

To view the report, please follow the following steps:

1. Download the .zip file from the report link above.

2. Extract all the contents of the downloaded .zip file to your desktop.

3. Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").

4. Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the report, there are links to view all the analyses performed for the project.

V. Raw Sequence Data Download

The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files. Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1, with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”. The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence and its corresponding quality scores. Most sequence analysis software will be able to open them. The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:


Sample ID Original Sample ID Read 1 File Name Read 2 File Name
S01 zr4083_10V1V3_R1.fastq.gz zr4083_10V1V3_R2.fastq.gz
S02 zr4083_11V1V3_R1.fastq.gz zr4083_11V1V3_R2.fastq.gz
S03 zr4083_12V1V3_R1.fastq.gz zr4083_12V1V3_R2.fastq.gz
S04 zr4083_13V1V3_R1.fastq.gz zr4083_13V1V3_R2.fastq.gz
S05 zr4083_14V1V3_R1.fastq.gz zr4083_14V1V3_R2.fastq.gz
S06 zr4083_15V1V3_R1.fastq.gz zr4083_15V1V3_R2.fastq.gz
S07 zr4083_16V1V3_R1.fastq.gz zr4083_16V1V3_R2.fastq.gz
S08 zr4083_17V1V3_R1.fastq.gz zr4083_17V1V3_R2.fastq.gz
S09 zr4083_18V1V3_R1.fastq.gz zr4083_18V1V3_R2.fastq.gz
S10 zr4083_19V1V3_R1.fastq.gz zr4083_19V1V3_R2.fastq.gz
S11 zr4083_1V1V3_R1.fastq.gz zr4083_1V1V3_R2.fastq.gz
S12 zr4083_20V1V3_R1.fastq.gz zr4083_20V1V3_R2.fastq.gz
S13 zr4083_2V1V3_R1.fastq.gz zr4083_2V1V3_R2.fastq.gz
S14 zr4083_3V1V3_R1.fastq.gz zr4083_3V1V3_R2.fastq.gz
S15 zr4083_4V1V3_R1.fastq.gz zr4083_4V1V3_R2.fastq.gz
S16 zr4083_5V1V3_R1.fastq.gz zr4083_5V1V3_R2.fastq.gz
S17 zr4083_6V1V3_R1.fastq.gz zr4083_6V1V3_R2.fastq.gz
S18 zr4083_7V1V3_R1.fastq.gz zr4083_7V1V3_R2.fastq.gz
S19 zr4083_8V1V3_R1.fastq.gz zr4083_8V1V3_R2.fastq.gz
S20 zr4083_9V1V3_R1.fastq.gz zr4083_9V1V3_R2.fastq.gz
S21 zr4106_1V1V3_R1.fastq.gz zr4106_1V1V3_R2.fastq.gz
S22 zr4106_2V1V3_R1.fastq.gz zr4106_2V1V3_R2.fastq.gz
S23 zr4106_3V1V3_R1.fastq.gz zr4106_3V1V3_R2.fastq.gz
S24 zr4106_4V1V3_R1.fastq.gz zr4106_4V1V3_R2.fastq.gz
S25 zr4106_5V1V3_R1.fastq.gz zr4106_5V1V3_R2.fastq.gz
S26 zr4106_6V1V3_R1.fastq.gz zr4106_6V1V3_R2.fastq.gz
S27 zr4106_7V1V3_R1.fastq.gz zr4106_7V1V3_R2.fastq.gz
S28 zr4106_8V1V3_R1.fastq.gz zr4106_8V1V3_R2.fastq.gz
S29 zr4106_9V1V3_R1.fastq.gz zr4106_9V1V3_R2.fastq.gz
S30 zr4511_1V1V3_R1.fastq.gz zr4511_1V1V3_R2.fastq.gz
S31 zr4511_2V1V3_R1.fastq.gz zr4511_2V1V3_R2.fastq.gz
S32 zr4917_1V1V3_R1.fastq.gz zr4917_1V1V3_R2.fastq.gz
S33 zr4917_2V1V3_R1.fastq.gz zr4917_2V1V3_R2.fastq.gz
S34 zr4917_3V1V3_R1.fastq.gz zr4917_3V1V3_R2.fastq.gz
S35 zr4917_4V1V3_R1.fastq.gz zr4917_4V1V3_R2.fastq.gz
S36 zr4917_5V1V3_R1.fastq.gz zr4917_5V1V3_R2.fastq.gz
S37 zr4917_6V1V3_R1.fastq.gz zr4917_6V1V3_R2.fastq.gz
S38 zr4917_7V1V3_R1.fastq.gz zr4917_7V1V3_R2.fastq.gz
S39 zr4917_8V1V3_R1.fastq.gz zr4917_8V1V3_R2.fastq.gz
S40 zr4917_9V1V3_R1.fastq.gz zr4917_9V1V3_R2.fastq.gz

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Raw sequence data download link:

VI. Analysis - DADA2 Read Processing

What is DADA2?

DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. DADA2 identified more real variants and output fewer spurious sequences than other methods.

DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information, which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances, whereas most other methods use abundance ranks if they use abundance at all. The DADA2 error model identifies the differences between sequences, eg. A->C, whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself, rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.

DADA2 Publication: Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23. PMID: 27214047; PMCID: PMC4927377.

DADA2 Software Package is available as an R package at : https://benjjneb.github.io/dada2/index.html

Analysis Procedures:

DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:

Step 1. Read trimming based on sequence quality The quality of NGS Illumina sequences often decreases toward the end of the reads. DADA2 allows to trim off the poor quality read ends in order to improve the error model building and pair mergicing performance.

Step 2. Learn the Error Rates The DADA2 algorithm makes use of a parametric error model (err) and every amplicon dataset has a different set of error rates. The learnErrors method learns this error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors).

Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising". The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.

Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences. Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding denoised reverse reads, and then constructing the merged “contig” sequences. By default, merged sequences are only output if the forward and reverse reads overlap by at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).

Step 5. Remove chimera. The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.

Results

1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines. The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next section “Optimal trim length for ASVs”.

Below is the link to a PDF file for viewing the quality plots for all samples:

2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline. In order to achieve highest number of ASVs, an empirical approach was used -

Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data

Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):

R1/R2	281	271	261	251	241	231
321	27.49%	38.42%	44.65%	45.96%	48.37%	44.33%
311	28.27%	40.15%	45.87%	47.53%	46.13%	29.09%
301	28.67%	40.16%	45.97%	43.27%	29.04%	11.99%
291	28.55%	39.56%	41.81%	27.38%	12.60%	11.43%
281	29.99%	36.71%	26.01%	11.57%	11.72%	9.20%
271	25.82%	23.96%	10.94%	10.56%	9.03%	3.48%

Based on the above result, the trim length combination of R1 = 321 bases and R2 = 241 bases (highlighted red above), was chosen for generating final ASVs for all sequences. This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.

3. Error plots from learning the error rates After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates. The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop with increased quality as expected.

Forward Read R1 Error Plot

Reverse Read R2 Error Plot

The PDF version of these plots are available here:

4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis, tracking paired read counts of each samples for all the steps during DADA2 denoising process - including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).

Sample ID	F4083.S01	F4083.S02	F4083.S03	F4083.S04	F4083.S05	F4083.S06	F4083.S07	F4083.S08	F4083.S09	F4083.S10	F4083.S11	F4083.S12	F4083.S13	F4083.S14	F4083.S15	F4083.S16	F4083.S17	F4083.S18	F4083.S19	F4083.S20	F4106.S21	F4106.S22	F4106.S23	F4106.S24	F4106.S25	F4106.S26	F4106.S27	F4106.S28	F4106.S29	F4511.S30	F4511.S31	F4917.S32	F4917.S33	F4917.S34	F4917.S35	F4917.S36	F4917.S37	F4917.S38	F4917.S39	F4917.S40	Row Sum	Percentage
input	25,392	27,378	25,425	27,851	23,145	23,565	24,941	23,728	24,749	31,000	19,731	21,438	28,106	26,831	24,793	29,330	20,896	16,722	26,285	21,821	30,575	33,770	36,605	39,287	39,557	37,580	35,732	39,788	29,892	34,942	49,462	29,049	23,810	29,634	35,042	35,354	22,399	26,652	25,191	24,171	1,151,619	100.00%
filtered	25,392	27,377	25,421	27,849	23,142	23,564	24,937	23,723	24,746	30,997	19,728	21,435	28,103	26,827	24,787	29,325	20,892	16,721	26,280	21,820	30,540	33,730	36,571	39,248	39,523	37,532	35,700	39,747	29,862	34,863	49,350	29,025	23,797	29,616	35,029	35,340	22,381	26,639	25,178	24,158	1,150,895	99.94%
denoisedF	23,896	26,168	24,458	26,424	21,893	22,369	23,671	22,324	23,419	29,638	18,437	20,419	26,912	25,426	23,556	28,204	19,461	15,626	24,932	20,330	29,357	32,756	35,466	37,816	38,309	35,894	33,885	38,195	28,756	33,110	47,992	27,464	21,821	28,428	33,560	33,135	21,224	25,523	23,860	22,820	1,096,934	95.25%
denoisedR	24,194	25,941	24,293	26,327	21,799	22,337	23,781	22,576	23,471	29,356	18,462	20,332	26,912	25,150	23,635	28,049	19,606	15,837	25,098	20,732	29,373	32,499	35,473	37,732	38,190	35,863	34,329	38,049	28,681	31,263	47,317	27,541	22,435	28,514	33,836	33,796	21,304	25,488	23,933	22,999	1,096,503	95.21%
merged	20,940	22,598	21,149	22,153	18,384	18,459	20,187	19,535	19,211	25,356	13,808	17,530	23,771	20,388	19,379	24,417	15,262	12,543	20,934	16,717	26,271	29,306	32,005	32,600	32,243	30,152	28,344	32,125	23,842	25,300	40,975	22,967	16,393	24,162	29,362	28,415	18,475	20,783	20,109	18,534	925,084	80.33%
nonchim	9,657	9,671	8,770	11,538	10,210	9,025	8,987	9,039	9,362	11,281	7,763	8,074	11,750	10,828	10,369	10,257	8,189	6,963	8,664	8,663	12,255	14,334	15,482	17,779	16,433	17,297	16,923	18,380	14,284	14,363	17,764	12,788	10,625	13,736	15,398	16,307	10,560	13,543	12,498	12,351	482,160	41.87%

This table can be downloaded as an Excel table below:

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 6902 unique merged and chimera-free ASV sequences were identified, and their corresponding read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.

The table can be downloaded from this link:

Sample Meta Information

#SampleID	Previous ID	sequence file name	Group
F4083.S11	F4083.01	zr4083_1V1V3	CF
F4083.S13	F4083.02	zr4083_2V1V3	CF
F4083.S14	F4083.03	zr4083_3V1V3	CF
F4083.S15	F4083.04	zr4083_4V1V3	SECC
F4083.S16	F4083.05	zr4083_5V1V3	CF
F4083.S17	F4083.06	zr4083_6V1V3	SECC
F4083.S18	F4083.07	zr4083_7V1V3	SECC
F4083.S19	F4083.08	zr4083_8V1V3	SECC
F4083.S20	F4083.09	zr4083_9V1V3	SECC
F4083.S01	F4083.10	zr4083_10V1V3	SECC
F4083.S02	F4083.11	zr4083_11V1V3	SECC
F4083.S03	F4083.12	zr4083_12V1V3	CF
F4083.S04	F4083.13	zr4083_13V1V3	SECC
F4083.S05	F4083.14	zr4083_14V1V3	SECC
F4083.S06	F4083.15	zr4083_15V1V3	SECC
F4083.S07	F4083.16	zr4083_16V1V3	SECC
F4083.S08	F4083.17	zr4083_17V1V3	SECC
F4083.S09	F4083.18	zr4083_18V1V3	SECC
F4083.S10	F4083.19	zr4083_19V1V3	SECC
F4083.S12	F4083.20	zr4083_20V1V3	SECC
F4106.S21	F4106.01	zr4106_1V1V3	SECC
F4106.S22	F4106.02	zr4106_2V1V3	SECC
F4106.S23	F4106.03	zr4106_3V1V3	SECC
F4106.S24	F4106.04	zr4106_4V1V3	CF
F4106.S25	F4106.05	zr4106_5V1V3	CF
F4106.S26	F4106.06	zr4106_6V1V3	SECC
F4106.S27	F4106.07	zr4106_7V1V3	CF
F4106.S28	F4106.08	zr4106_8V1V3	SECC
F4106.S29	F4106.09	zr4106_9V1V3	CF
F4511.S30	F4511.01	zr4511_1V1V3	CF
F4511.S31	F4511.02	zr4511_2V1V3	CF
F4917.S32	F4917.01	zr4917_1V1V3	CF
F4917.S33	F4917.02	zr4917_2V1V3	SECC
F4917.S34	F4917.03	zr4917_3V1V3	SECC
F4917.S35	F4917.04	zr4917_4V1V3	SECC
F4917.S36	F4917.05	zr4917_5V1V3	SECC
F4917.S37	F4917.06	zr4917_6V1V3	SECC
F4917.S38	F4917.07	zr4917_7V1V3	CF
F4917.S39	F4917.08	zr4917_8V1V3	CF
F4917.S40	F4917.09	zr4917_9V1V3	CF

ASV Read Counts by Samples

#Sample ID	Read Count
F4083.S18	6,963
F4083.S11	7,763
F4083.S12	8,074
F4083.S17	8,189
F4083.S20	8,663
F4083.S19	8,664
F4083.S03	8,770
F4083.S07	8,987
F4083.S06	9,025
F4083.S08	9,039
F4083.S09	9,362
F4083.S01	9,657
F4083.S02	9,671
F4083.S05	10,210
F4083.S16	10,257
F4083.S15	10,369
F4917.S37	10,560
F4917.S33	10,625
F4083.S14	10,828
F4083.S10	11,281
F4083.S04	11,538
F4083.S13	11,750
F4106.S21	12,255
F4917.S40	12,351
F4917.S39	12,498
F4917.S32	12,788
F4917.S38	13,543
F4917.S34	13,736
F4106.S29	14,284
F4106.S22	14,334
F4511.S30	14,363
F4917.S35	15,398
F4106.S23	15,482
F4917.S36	16,307
F4106.S25	16,433
F4106.S27	16,923
F4106.S26	17,297
F4511.S31	17,764
F4106.S24	17,779
F4106.S28	18,380

VII. Analysis - Read Taxonomy Assignment

Read Taxonomy Assignment - Methods

The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline

Version 20210310

1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences. It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ), HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG) (http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) , and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz). These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences, as well as sequences with poor taxonomy annotation (e.g., without species information). This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences. Altogether these sequence represent a total of 15,601 oral and non-oral microbial species.

The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters. Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length (i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate the sequence percent identity) were classified based on the taxonomy of the reference sequence with highest sequence identity. If a read matched with reference sequences representing more than one species with equal percent identity and alignment length, it was subject to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species best hits were considered valid and were assigned with a unique species notation (e.g., spp) denoting unresolvable multiple species.

2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were removed. The remaining reads were subject to the de novo operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010). The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU. The output of this step produced species-level de novo clustered OTUs with 98% identity. Representative reads from each of the OTUs/species were then BLASTN-searched against the same reference sequence set again to determine the closest species for these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in the previous step, for down-stream analyses.

Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.

3. Designations used in the taxonomy:

	1) Taxonomy levels are indicated by these prefixes:
	
	   k__: domain/kingdom
	   p__: phylum
	   c__: class
	   o__: order
	   f__: family
	   g__: genus  
	   s__: species
	
	   Example: 
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
		
	2) Unique level identified – known species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
	
	   The above example shows some reads match to a single species (all levels are unique)
	
	3) Non-unique level identified – known species:

	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
	   
	   The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the 
	   genus Roseburia; the “spp123” is a temporally assigned species ID.
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
	   
	   The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.; 
	   the “spp234” is a temporally assigned species ID.
	
	4) Unique level identified – unknown species, potential novel species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
	   
	   The above example indicates that some reads have no match to any of the reference sequences with 
	   sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. However this groups 
	   of reads (actually the representative read from a de novo  OTU) has 96% percent identity to 
	   Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis. 
	   (But they are not the same species).
	
	5) Multiple level identified – unknown species, potential novel species:
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
	
	   The above example indicates that some reads have no match to any of the reference sequences 
	   with sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. 
	   However this groups of reads (actually the representative read from a de novo  OTU) 
	   has 96% percent identity equally to 3 species in Roseburia. Thus this is no single 
	   closest species, instead this group of reads match equally to multiple species at 96%. 
	   Since they have passed chimera check so they represent a novel species. “sppn123” is a 
	   temporary ID for this potential novel species.

4. The taxonomy assignment algorithm is illustrated in this flow char below:

Read Taxonomy Assignment - Result Summary *

Code	Category	MPC=0% (>=1 read)	MPC=0.1%(>=481 reads)
A	Total reads	482,160	482,160
B	Total assigned reads	481,207	481,207
C	Assigned reads in species with read count < MPC	0	31,033
D	Assigned reads in samples with read count < 500	0	0
E	Total samples	40	40
F	Samples with reads >= 500	40	40
G	Samples with reads < 500	0	0
H	Total assigned reads used for analysis (B-C-D)	481,207	450,174
I	Reads assigned to single species	449,410	427,295
J	Reads assigned to multiple species	18,057	16,563
K	Reads assigned to novel species	13,740	6,316
L	Total number of species	409	133
M	Number of single species	266	122
N	Number of multi-species	19	5
O	Number of novel species	124	6
P	Total unassigned reads	953	953
Q	Chimeric reads	189	189
R	Reads without BLASTN hits	4	4
S	Others: short, low quality, singletons, etc.	760	760
	A=B+P=C+D+H+Q+R+S
	E=F+G
	B=C+D+H
	H=I+J+K
	L=M+N+O
	P=Q+R+S

* MPC = Minimal percent (of all assigned reads) read count per species, species with read count < MPC were removed.

* Samples with reads < 500 were removed from downstream analyses.

* The assignment result from MPC=0.1% was used in the downstream analyses.

Read Taxonomy Assignment - ASV Species-Level Read Counts Table

This table shows the read counts for each sample (columns) and each species identified based on the ASV sequences. The downstream analyses were based on this table.

SPID	Taxonomy	F4083.S01	F4083.S02	F4083.S03	F4083.S04	F4083.S05	F4083.S06	F4083.S07	F4083.S08	F4083.S09	F4083.S10	F4083.S11	F4083.S12	F4083.S13	F4083.S14	F4083.S15	F4083.S16	F4083.S17	F4083.S18	F4083.S19	F4083.S20	F4106.S21	F4106.S22	F4106.S23	F4106.S24	F4106.S25	F4106.S26	F4106.S27	F4106.S28	F4106.S29	F4511.S30	F4511.S31	F4917.S32	F4917.S33	F4917.S34	F4917.S35	F4917.S36	F4917.S37	F4917.S38	F4917.S39	F4917.S40
SP1	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._str._C300	1543	192	1497	1063	0	483	923	1938	945	886	94	68	365	173	88	919	272	0	546	480	866	687	1137	24	123	454	260	733	422	318	1217	458	1036	1852	676	247	4029	0	0	325
SP10	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp. HMT930	20	0	0	178	206	26	0	140	38	54	0	0	0	119	0	100	44	0	0	122	0	37	240	0	64	138	0	0	147	0	0	111	0	178	0	64	0	104	0	0
SP100	Bacteria;Saccharibacteria_(TM7);Saccharibacteria_(TM7)_[C-1];Saccharibacteria_(TM7)_[O-1];Saccharibacteria_(TM7)_[F-1];Saccharibacteria_(TM7)_[G-1];bacterium HMT348	0	0	0	158	16	0	0	18	34	25	48	32	4	129	0	0	0	22	15	46	100	0	0	71	127	159	33	142	78	289	0	0	275	0	49	0	0	0	33	0
SP101	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;sp. HMT908	0	129	0	9	105	43	0	98	81	52	0	0	43	122	0	92	81	0	51	0	0	0	0	0	13	0	0	143	0	0	0	0	0	0	0	0	0	57	366	0
SP104	Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnoanaerobaculum;umeaense	0	0	0	76	0	0	0	0	58	66	118	0	0	0	0	90	0	0	0	0	0	0	0	128	0	0	207	0	0	0	0	0	0	0	0	205	0	135	214	0
SP105	Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;sp._Oral_Taxon_B07	0	0	0	0	0	0	71	0	0	0	0	0	0	0	0	0	0	61	0	0	0	0	16	0	0	0	0	132	0	0	0	0	0	0	0	579	0	123	0	0
SP106	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;histicola	0	124	0	104	112	0	34	27	0	0	0	0	0	0	0	0	49	0	0	0	16	276	0	0	0	208	0	0	121	0	12	0	0	0	0	0	0	0	0	0
SP108	Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Gemella;moribillum	0	0	0	0	0	84	0	0	0	47	0	11	0	104	0	121	0	0	81	0	0	0	119	0	42	116	153	0	0	109	0	0	0	213	181	0	0	0	77	138
SP109	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;haemolyticus	172	0	66	66	89	156	115	33	63	0	0	0	0	0	0	0	53	141	93	291	168	0	250	188	185	0	111	0	238	0	0	161	0	516	81	511	0	129	41	0
SP11	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;catoniae	0	0	31	37	0	52	0	0	66	131	88	14	0	87	89	0	0	37	51	36	0	0	0	295	217	61	133	270	307	132	94	0	0	120	0	0	20	261	242	0
SP110	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT219	0	0	0	0	0	0	0	0	0	8	16	0	14	0	0	0	0	0	0	0	0	0	0	0	0	0	167	11	0	0	115	86	0	0	0	0	0	124	61	0
SP112	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT308	55	0	0	0	162	0	34	65	0	0	0	0	0	40	48	44	0	0	0	24	90	0	0	112	0	0	0	0	0	0	0	0	0	0	87	0	44	0	0	89
SP114	Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-3];bacterium HMT100	0	0	0	50	0	0	12	0	28	0	79	0	59	35	93	0	0	0	0	0	0	0	0	69	42	0	158	0	31	0	0	0	0	0	40	0	0	33	0	0
SP116	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;pallens	0	0	13	0	95	0	0	0	24	0	0	0	0	159	0	0	31	0	0	0	208	0	9	107	0	0	25	0	0	0	0	0	0	0	0	127	0	0	0	0
SP117	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT912	0	0	0	72	0	63	158	0	32	88	0	0	0	0	0	0	0	42	0	39	0	0	0	0	0	0	34	0	0	0	0	0	0	0	0	147	0	33	0	50
SP118	Bacteria;Saccharibacteria_(TM7);Saccharibacteria_(TM7)_[C-1];Saccharibacteria_(TM7)_[O-1];Saccharibacteria_(TM7)_[F-1];Saccharibacteria_(TM7)_[G-1];bacterium HMT346	0	0	0	95	100	0	22	24	35	0	0	0	0	42	0	0	0	0	13	0	0	0	0	0	0	184	0	0	0	235	0	0	96	0	0	0	0	0	0	0
SP12	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;parahaemolyticus	371	87	0	0	0	0	67	0	0	0	0	0	0	254	0	0	0	51	0	0	0	21	0	80	76	0	0	0	0	0	0	0	0	124	0	0	0	166	187	0
SP120	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp. HMT248	0	0	0	0	0	0	0	0	0	37	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	525	0	210	0	0
SP121	Bacteria;Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Ruminococcaceae_[G-1];bacterium HMT075	0	0	0	43	63	0	69	0	0	25	0	0	0	81	0	0	27	0	17	0	0	0	40	0	0	53	16	25	0	142	0	0	59	0	65	109	0	29	0	0
SP122	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;salivarius	0	58	8	0	42	17	0	186	0	0	0	0	0	0	63	0	0	156	0	0	121	184	0	83	0	88	0	0	0	0	51	0	0	0	0	0	0	0	0	0
SP13	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;paraphrohaemolyticus	117	0	70	119	0	0	345	47	0	198	178	0	53	102	198	82	0	10	0	0	0	0	242	138	0	0	0	0	0	0	0	309	0	0	0	36	0	0	0	117
SP130	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;gingivalis	0	0	28	0	0	37	0	0	0	0	51	77	78	74	0	0	0	0	84	27	0	0	0	0	58	33	196	137	0	0	382	201	0	0	0	0	0	168	0	0
SP131	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;cinerea	0	0	0	0	0	47	0	0	47	102	0	0	0	141	48	16	0	0	0	113	0	111	123	0	0	125	29	0	0	0	34	324	0	35	0	0	0	149	371	0
SP132	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces;sp. HMT175	0	0	0	59	0	0	0	0	163	0	0	0	50	0	94	0	0	0	0	0	0	0	0	0	54	112	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SP137	Bacteria;Saccharibacteria_(TM7);Saccharibacteria_(TM7)_[C-1];Saccharibacteria_(TM7)_[O-1];Saccharibacteria_(TM7)_[F-1];Saccharibacteria_(TM7)_[G-1];bacterium HMT347	14	94	0	23	0	0	0	0	20	0	48	0	0	28	78	0	0	0	0	0	0	0	0	0	0	132	0	0	57	0	0	0	0	0	0	0	0	0	0	0
SP138	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._dentisani_clade_058	19	0	0	0	0	0	0	54	0	14	0	0	0	17	0	0	0	0	0	0	0	0	0	17	0	0	0	0	131	122	0	0	0	0	0	122	0	0	0	0
SP139	Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sputigena	0	0	0	46	106	0	0	12	18	0	0	0	0	31	0	0	26	0	12	0	103	113	0	0	0	61	26	0	0	0	0	0	97	0	13	0	0	0	0	0
SP14	Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Lautropia;mirabilis	155	69	195	69	38	0	0	31	330	0	0	0	19	127	25	172	0	84	104	148	0	0	216	316	0	84	264	392	81	402	275	164	68	0	270	0	151	0	40	59
SP142	Bacteria;Actinobacteria;Actinomycetia;Actinomycetales;Actinomycetaceae;Actinomyces;naeslundii	0	0	0	0	0	0	0	0	61	0	0	151	0	0	0	0	0	0	0	0	0	0	21	170	142	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SP144	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sputigena	17	37	76	0	0	0	0	0	117	0	146	0	75	0	146	51	0	30	45	0	0	0	52	54	35	172	105	212	62	126	105	73	0	0	115	0	53	163	0	0
SP145	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp. HMT278	22	0	0	0	0	60	0	14	0	0	0	0	43	0	0	0	0	0	0	0	0	0	0	0	0	0	103	0	0	0	0	63	0	224	52	108	0	0	59	0
SP147	Bacteria;Proteobacteria;Gammaproteobacteria;Cardiobacteriales;Cardiobacteriaceae;Cardiobacterium;valvarum	0	0	0	0	0	0	0	0	0	0	56	84	0	0	0	18	0	0	0	0	0	0	0	0	116	0	0	0	0	0	28	0	0	0	0	0	0	182	153	54
SP148	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces;sp._str._ChDCB197	0	0	0	0	0	0	0	0	0	0	147	0	0	0	0	0	0	0	0	0	0	164	0	0	150	0	87	0	0	0	0	0	0	32	0	0	0	0	0	0
SP15	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Leptotrichiaceae;Leptotrichia;hofstadii	37	42	0	0	25	0	0	0	8	27	0	0	59	9	814	94	0	0	44	85	0	0	0	630	329	63	0	449	250	645	0	0	0	0	0	0	0	0	0	0
SP150	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;salivae	0	0	0	64	64	8	0	0	0	0	0	0	0	32	0	0	0	0	0	0	147	223	0	0	0	398	0	9	15	0	0	0	0	0	0	0	0	0	0	0
SP152	Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Corynebacteriaceae;Corynebacterium;matruchotii	0	0	0	52	0	0	0	0	0	0	103	0	0	80	0	0	0	50	0	0	0	0	0	136	338	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SP155	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;sp. HMT036	0	0	0	73	78	58	27	0	0	151	48	32	0	202	37	35	0	172	68	0	0	0	85	0	0	0	250	0	107	0	69	210	0	0	0	113	0	0	0	227
SP16	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;mutans	2	65	0	73	111	47	6	49	0	0	0	78	0	0	0	0	29	0	131	93	238	353	0	145	141	265	0	33	0	0	0	0	280	6	0	0	9	0	0	0
SP166	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;trevisanii	0	0	0	0	0	0	0	0	0	35	25	0	33	0	0	0	32	0	12	0	0	0	0	92	510	0	71	55	0	48	0	0	0	0	0	0	0	59	86	0
SP17	Bacteria;Actinobacteria;Actinomycetia;Actinomycetales;Actinomycetaceae;Actinomyces;johnsonii	0	0	0	0	0	0	34	0	0	0	0	0	0	0	0	0	0	20	0	0	0	0	0	49	43	0	45	0	221	143	0	0	0	0	0	0	0	373	0	728
SP173	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oulorum	0	22	0	0	73	0	0	0	0	0	0	0	0	16	133	0	40	0	0	10	125	161	0	10	0	295	0	22	0	0	193	0	0	0	0	0	0	0	0	0
SP175	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;veroralis	0	111	0	19	0	0	0	23	0	0	0	0	0	0	0	0	0	0	34	0	0	281	0	0	0	0	0	0	52	0	0	0	0	0	0	0	0	0	0	0
SP180	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT913	0	0	33	123	0	161	44	38	0	24	27	0	0	111	0	17	46	0	0	0	0	0	0	0	0	23	61	37	105	0	0	0	0	0	0	101	0	75	0	68
SP181	Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoanaerobaculum;orale	0	0	0	0	42	0	78	0	0	0	0	40	156	0	0	0	0	0	0	0	87	138	148	37	0	0	0	0	0	191	0	0	0	0	165	0	0	0	0	39
SP183	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;sp. HMT949	0	0	0	117	0	61	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	157	0	0	0	136	333	0	0	0	0	0	0	0	0	0	0	0
SP184	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT417	0	0	0	53	0	81	0	151	109	0	0	0	0	0	0	0	209	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SP185	Bacteria;Actinobacteria;Actinomycetia;Propionibacteriales;Propionibacteriaceae;Arachnia;propionica	0	0	119	97	0	13	6	14	124	120	81	0	36	26	93	0	38	33	62	80	0	0	0	583	262	54	205	63	0	74	155	52	0	0	0	62	0	234	69	129
SP19	Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;elegans	38	176	0	261	342	398	392	0	147	575	62	178	87	530	145	306	160	43	283	390	320	233	718	0	466	366	149	545	430	0	117	705	690	478	105	477	227	464	897	308
SP195	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;subflava	0	0	0	56	0	0	57	0	0	0	0	0	0	230	0	0	0	0	0	0	0	0	0	161	0	0	0	0	0	275	0	0	0	0	0	211	0	0	0	0
SP196	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces;massiliensis	0	0	0	0	0	0	0	0	55	0	0	0	0	0	0	0	0	0	0	0	0	0	0	103	417	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SP2	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;parainfluenzae	466	196	1570	358	82	400	162	403	746	211	170	0	1392	208	144	251	247	369	214	260	221	0	709	882	104	334	777	473	285	125	471	1158	0	950	2486	0	1262	551	879	445
SP20	Bacteria;Firmicutes;Bacilli;Lactobacillales;Aerococcaceae;Abiotrophia;defectiva	243	93	158	122	20	177	186	71	185	180	63	0	86	85	168	198	0	81	308	141	0	15	499	187	204	27	186	181	79	0	0	286	110	766	544	450	287	123	76	0
SP204	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp. HMT206	61	0	0	0	0	74	71	0	12	16	0	0	0	30	0	6	30	0	36	114	0	175	0	0	0	102	0	0	0	0	117	78	0	0	0	30	0	238	0	96
SP21	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sanguinis	653	0	283	301	113	73	197	199	496	225	206	29	2574	84	528	467	0	133	386	398	589	239	779	692	805	283	647	599	102	438	865	855	715	1607	1117	269	596	725	175	670
SP215	Bacteria;Bacteroidetes;Bacteroides;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._Oral_Taxon_B43	17	0	0	0	0	103	58	0	0	0	0	0	0	63	0	0	0	56	0	0	0	0	0	0	0	0	0	109	0	185	0	0	0	0	0	0	0	0	0	0
SP22	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;infantis	0	0	200	33	0	0	179	101	0	0	60	0	0	0	64	56	0	360	0	141	0	0	269	0	0	0	140	0	0	0	36	144	0	0	0	0	0	46	0	0
SP23	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT212	37	58	93	63	0	0	74	83	146	0	90	0	142	77	0	31	121	28	0	75	0	0	124	157	305	174	241	324	15	296	131	0	0	111	269	0	0	147	220	148
SP24	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;sp. HMT898	0	0	97	0	0	0	0	0	0	0	0	0	0	44	0	85	0	0	0	0	0	0	0	44	72	0	0	55	0	0	0	0	0	0	0	0	0	281	0	0
SP25	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Kingella;oralis	18	119	0	87	0	0	34	49	75	46	110	80	89	19	77	0	0	0	100	75	17	200	0	131	115	0	210	0	85	0	23	0	0	0	15	0	133	134	0	161
SP26	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT314	0	0	0	26	86	66	0	0	54	0	0	0	0	0	0	0	23	0	0	0	651	0	0	0	0	0	0	0	16	0	0	0	0	0	0	83	0	0	0	0
SP27	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Leptotrichiaceae;Leptotrichia;shahii	0	0	0	13	44	157	0	219	1	56	0	0	104	67	145	153	254	0	201	0	0	0	0	0	2	10	0	217	423	0	0	0	0	0	0	274	0	204	0	512
SP29	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum	0	0	0	0	0	0	0	0	0	120	144	0	165	86	0	0	180	0	0	0	0	0	0	416	137	0	1167	652	235	257	0	0	0	0	64	0	386	0	178	0
SP3	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;mitis	891	717	253	799	1355	450	476	67	283	1266	429	1589	872	704	149	659	371	514	512	711	539	730	1074	920	1053	782	797	1548	451	350	1015	1437	563	560	392	542	37	317	1361	786
SP30	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;parasanguinis_II	14	135	0	191	0	21	31	271	0	0	0	13	0	0	0	0	55	0	0	0	89	103	0	60	0	150	0	0	50	0	27	0	0	0	0	0	0	0	0	0
SP33	Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;paradiacens	0	0	0	56	76	0	136	0	0	133	0	0	0	107	114	62	195	180	64	0	0	367	0	69	0	176	0	0	289	33	0	0	0	244	433	0	0	109	0	0
SP35	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;flavescens\|subflava	89	0	255	0	43	0	36	0	42	37	98	0	105	0	87	120	99	75	36	78	127	0	39	259	0	147	262	243	0	0	131	136	0	128	398	91	0	182	501	0
SP36	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT473	122	143	0	75	344	980	87	131	220	847	0	192	0	356	48	371	211	118	390	415	0	0	121	0	264	372	171	223	206	0	0	408	0	0	0	1472	269	129	0	264
SP37	Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Veillonella;parvula	639	1980	212	1096	413	254	287	860	658	81	344	450	316	447	462	270	461	146	656	581	1008	2826	991	1072	1188	1123	1267	661	595	283	1711	501	912	467	411	288	1257	634	343	1280
SP38	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;cristatus	274	132	40	38	310	644	261	179	298	1178	9	137	337	238	161	541	233	54	182	166	0	162	106	1291	995	514	293	453	884	383	59	180	366	310	362	553	58	360	177	79
SP39	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT225	0	0	0	41	0	0	96	0	29	66	49	0	0	0	0	0	0	0	0	0	0	0	0	82	0	73	48	0	0	0	44	0	147	0	0	0	0	0	0	0
SP4	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._tigurinus_clade_070	0	0	0	62	41	0	17	372	0	20	0	0	0	19	0	0	146	0	0	0	0	0	0	0	0	0	0	0	0	0	28	0	228	0	0	63	0	0	0	0
SP40	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis	635	779	0	189	131	19	228	189	114	318	0	77	474	83	253	400	439	75	477	341	77	322	1239	352	564	983	271	128	267	483	38	712	359	841	402	258	145	46	0	114
SP41	Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;sp. HMT780	0	162	0	95	215	292	0	0	135	163	0	179	0	105	52	197	144	35	146	172	363	59	51	0	350	155	0	107	346	0	37	157	269	0	0	0	122	366	0	496
SP42	Bacteria;Firmicutes;Bacilli;Bacillales;Staphylococcaceae;Gemella;haemolysans	114	169	42	22	249	116	110	110	0	247	54	122	80	188	45	182	66	162	210	150	214	81	391	224	204	0	128	150	242	0	104	150	180	726	71	369	0	232	746	189
SP44	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Micrococcaceae;Rothia;dentocariosa	0	105	0	96	0	31	110	0	35	0	61	174	238	59	163	267	76	22	0	79	0	307	0	403	200	143	0	113	137	112	280	0	0	0	0	0	0	0	0	0
SP45	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Micrococcaceae;Rothia;mucilaginosa	0	76	220	53	0	0	201	167	0	117	115	0	147	0	0	0	33	184	0	100	0	176	0	270	0	0	0	0	0	0	110	0	0	0	0	0	0	0	0	0
SP47	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;sicca	0	462	0	51	139	364	273	18	413	102	237	109	376	244	314	551	196	161	41	477	0	110	739	0	0	487	245	490	335	0	85	135	0	1033	191	144	0	1071	315	175
SP48	Bacteria;Proteobacteria;Gammaproteobacteria;Cardiobacteriales;Cardiobacteriaceae;Cardiobacterium;hominis	16	21	43	26	0	0	0	0	0	0	40	106	114	0	57	0	0	19	52	0	0	0	0	292	81	219	288	146	102	115	193	166	0	0	51	0	0	97	141	73
SP49	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;pasteri	130	16	180	86	376	228	170	348	126	135	134	167	0	299	141	195	255	198	137	112	468	0	3025	64	0	359	498	242	614	247	0	167	209	126	284	228	133	283	203	161
SP5	Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;atypica	0	42	0	192	220	28	0	108	0	0	0	0	20	31	0	0	185	0	0	0	1001	895	0	0	0	541	0	0	118	0	0	0	449	0	0	161	0	0	0	0
SP51	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT914	0	0	64	23	42	65	124	67	0	81	32	13	0	147	0	0	31	284	5	0	0	0	158	0	0	0	0	0	204	0	0	0	0	114	84	282	0	0	0	0
SP53	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;elongata	0	0	82	124	0	28	25	7	0	0	101	0	0	0	55	63	137	0	0	0	0	0	12	92	23	0	47	114	0	0	150	0	0	0	247	0	18	77	0	0
SP54	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp._AF189244.1	20	485	325	0	223	164	8	0	31	152	91	567	448	269	1019	235	17	11	260	129	102	454	0	178	878	135	83	523	699	95	2586	189	155	219	47	0	82	225	390	625
SP55	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;wadei	0	257	0	289	377	135	157	0	0	0	0	0	0	168	0	0	70	0	0	0	0	219	0	316	179	269	0	0	0	0	259	0	408	0	0	60	0	0	0	0
SP57	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT498	0	0	0	216	88	0	171	185	0	0	0	0	0	119	0	0	0	0	0	0	211	209	0	192	0	205	0	0	246	390	0	0	519	0	0	0	0	0	0	0
SP58	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT317	0	0	0	0	0	146	105	66	0	0	0	0	0	213	495	0	130	0	58	148	0	0	0	362	323	429	347	663	306	719	0	0	0	0	0	0	0	0	468	164
SP6	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;sp. HMT513	0	0	0	61	0	94	0	0	0	0	143	0	95	0	0	77	132	114	0	0	0	0	0	0	0	171	0	0	0	0	0	0	0	0	351	582	0	0	0	0
SP60	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;mucosa	335	434	936	0	0	0	228	151	312	130	20	87	0	0	318	0	0	0	0	0	260	0	0	144	84	18	154	537	187	387	268	243	363	290	1006	0	0	0	0	118
SP61	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Micrococcaceae;Rothia;aeria	0	91	113	0	62	0	28	0	35	98	27	46	0	127	0	109	0	76	123	0	0	0	75	25	0	0	58	217	0	298	725	0	0	0	221	0	0	0	0	0
SP62	Bacteria;Actinobacteria;Actinobacteria;Corynebacteriales;Corynebacteriaceae;Corynebacterium;durum	0	20	35	0	0	22	0	0	0	85	0	0	38	26	34	122	0	0	0	0	0	0	0	89	198	0	0	68	0	0	0	49	0	0	0	0	0	0	0	0
SP63	Bacteria;Firmicutes;Bacilli;Bacillales;Gemellaceae;Gemella;morbillorum	34	0	0	68	56	51	102	35	146	107	0	0	46	193	92	0	23	129	63	54	0	0	0	23	180	81	0	359	0	662	0	0	200	67	225	238	0	66	0	0
SP64	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;sp. HMT458	0	0	0	82	0	85	46	0	0	74	45	0	0	104	246	68	30	33	190	92	0	0	0	509	417	377	172	508	241	442	145	0	0	0	0	0	0	62	75	0
SP66	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Kingella;denitrificans	93	84	0	0	0	0	67	0	45	0	0	0	0	136	141	68	90	0	217	34	85	0	0	139	142	5	2	456	190	0	685	0	0	0	273	0	27	0	109	49
SP67	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;granulosa	0	0	0	91	0	0	62	0	84	0	52	127	0	0	105	0	156	0	0	0	0	0	0	198	0	194	0	196	0	0	0	0	0	0	0	0	0	0	0	0
SP68	Bacteria;Saccharibacteria_(TM7);Saccharibacteria_(TM7)_[C-1];Saccharibacteria_(TM7)_[O-1];Saccharibacteria_(TM7)_[F-1];Saccharibacteria_(TM7)_[G-6];bacterium HMT870	0	0	0	0	0	29	108	148	27	172	0	96	0	0	73	0	39	0	0	25	49	111	0	0	0	318	0	0	88	0	0	0	0	146	239	0	36	0	0	0
SP7	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica	239	0	91	154	349	187	33	389	98	87	0	477	137	135	263	107	388	156	0	0	658	833	79	331	0	494	175	155	476	453	27	72	0	143	188	522	156	36	0	474
SP70	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp. HMT056	0	44	0	0	106	0	0	0	0	0	0	387	60	0	85	47	0	0	111	0	0	0	0	16	0	0	0	0	117	0	1306	0	0	0	0	0	0	0	0	307
SP72	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_B66	0	291	0	0	0	62	0	0	213	99	0	0	0	106	63	0	0	0	0	0	0	0	0	0	0	0	0	0	71	0	0	0	0	0	131	0	0	0	0	51
SP73	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;sp. HMT018	0	0	22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	0	250	0	0	0	232	265	0	0	0	0	0	0	0	0
SP74	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;sputorum	0	0	0	0	0	0	0	0	0	44	0	0	0	0	0	0	0	173	98	0	0	0	0	0	0	0	0	58	0	0	0	0	0	0	0	0	0	346	0	0
SP75	Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces;odontolyticus	0	0	0	0	0	38	0	90	76	0	44	0	0	51	0	0	0	48	0	40	0	0	0	78	0	0	0	0	46	0	0	0	0	0	0	0	0	0	0	0
SP76	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_ss_polymorphum	0	0	53	265	0	0	0	0	154	0	344	0	0	0	119	0	0	0	0	0	119	0	266	565	686	0	541	500	153	430	196	635	0	0	0	267	0	266	494	581
SP77	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;intermedius	21	75	0	0	15	0	0	28	0	6	0	107	39	0	14	70	11	7	0	0	0	35	0	12	25	149	83	41	3	14	52	0	0	0	4	0	31	45	0	15
SP78	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;loescheii	0	0	0	0	71	0	0	0	28	17	358	0	106	0	0	0	0	0	0	0	0	0	211	0	91	0	0	0	0	0	103	0	0	0	0	0	0	0	0	0
SP79	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;sp._Oral_Taxon_14	0	0	198	0	0	0	0	0	0	0	55	0	104	0	0	75	0	46	0	0	0	0	0	151	0	0	81	81	0	0	172	0	0	0	0	0	0	352	0	0
SP8	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;gordonii	227	0	0	226	47	0	24	146	189	109	0	54	0	60	77	164	63	18	91	90	185	159	0	27	0	115	33	0	152	0	0	0	422	0	0	158	0	32	32	177
SP80	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT215	62	0	106	0	0	0	0	0	14	0	27	0	11	145	68	6	29	55	0	0	0	0	99	7	0	0	67	0	13	51	0	20	0	0	63	0	68	0	0	38
SP81	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;buccalis	62	0	19	348	0	91	109	0	246	63	276	0	206	199	0	0	82	376	0	62	0	0	0	201	201	356	487	299	0	829	0	0	0	0	0	0	92	131	192	158
SP82	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;leadbetteri	0	0	0	55	0	68	0	50	56	27	231	40	69	68	76	0	153	64	48	30	16	0	0	126	35	136	164	379	176	0	205	0	0	0	166	105	0	85	90	54
SP84	Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens	155	156	0	115	0	0	109	51	144	0	0	0	0	44	0	69	0	0	89	227	262	113	0	0	0	0	0	0	0	140	91	0	523	299	458	314	397	0	0	0
SP85	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;aphrophilus	15	0	0	0	0	0	78	0	102	0	83	0	132	0	0	0	0	48	0	0	0	0	0	34	0	0	0	0	0	0	0	0	0	0	0	60	0	267	0	0
SP86	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oris	0	0	0	86	0	0	0	2	0	0	0	66	0	18	37	30	13	0	0	0	61	30	0	0	0	7	130	6	48	0	156	0	0	0	0	0	0	0	0	0
SP89	Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Eikenella;corrodens	0	0	0	21	26	52	0	0	0	0	41	59	42	0	0	18	41	0	0	26	0	0	42	0	0	74	177	0	0	44	0	139	0	0	0	0	0	104	227	0
SP9	Bacteria;Bacteroidetes;Bacteroides;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._Oral_Taxon_C34	0	0	0	0	0	143	85	0	0	0	48	0	0	32	0	0	0	0	160	109	0	49	0	0	0	0	0	0	0	0	0	0	0	0	0	145	0	0	0	319
SP90	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT221	15	47	0	158	77	0	0	0	0	0	0	0	79	0	0	0	0	0	0	0	0	185	0	0	0	392	0	0	0	0	0	0	98	38	0	0	0	0	0	56
SP93	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT306	0	22	0	22	42	0	0	0	60	0	0	0	0	64	0	9	28	0	0	0	212	0	0	0	0	0	35	0	0	0	0	0	0	0	0	0	0	0	0	0
SP94	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT392	16	0	14	89	0	41	85	0	62	120	177	0	105	114	0	13	17	129	0	0	0	0	0	103	46	146	263	91	19	546	0	36	0	14	141	108	0	120	104	60
SP95	Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;segnis	42	0	0	24	0	0	155	0	0	0	133	0	0	122	0	0	35	0	0	0	0	0	0	204	61	0	0	63	0	0	0	0	0	0	105	57	0	0	398	0
SP96	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;periodonticum	0	0	58	0	0	101	126	0	0	0	42	0	0	0	0	0	0	158	0	9	0	0	362	185	0	0	379	0	0	734	0	0	0	0	765	827	0	170	0	0
SP97	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Tannerella;sp. HMT286	0	0	0	80	15	54	0	47	0	16	86	0	31	55	93	30	104	36	34	68	0	0	0	212	143	130	229	108	24	177	0	0	0	0	42	0	0	39	85	36
SP98	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;chosunense	90	44	0	0	0	64	0	0	0	103	0	115	0	0	155	0	76	68	0	81	0	38	111	50	0	0	187	0	77	0	0	0	0	0	0	0	0	85	0	59
SP99	Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp. HMT322	32	19	138	16	19	22	56	33	67	0	69	0	104	39	0	28	22	33	82	0	0	0	55	88	119	12	143	141	106	0	107	208	0	214	324	47	57	138	0	45
SPN100	Bacteria;Proteobacteria;Gammaproteobacteria;Cardiobacteriales;Cardiobacteriaceae;Cardiobacterium;hominis_nov_97.313%	0	0	0	0	0	0	0	0	40	28	0	0	0	26	0	0	0	0	0	61	0	0	0	190	91	0	30	0	0	0	31	0	0	0	0	0	0	0	0	0
SPN53	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;catoniae_nov_97.909%	0	0	0	33	0	0	68	11	0	54	69	0	0	93	0	213	39	0	0	0	0	0	0	0	0	111	0	202	0	0	0	49	0	0	84	155	0	87	78	0
SPN65	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Streptobacillus;notomytis_nov_93.922%	0	0	0	0	105	87	43	92	0	41	0	0	0	0	0	0	0	0	0	73	0	0	0	0	0	199	0	99	0	0	0	0	0	0	0	104	0	0	227	242
SPN76	Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT473 nov_97.436%	0	0	60	0	0	0	0	0	0	0	0	0	0	60	0	0	0	43	0	0	0	0	40	0	0	0	0	0	85	0	0	0	0	38	26	489	0	0	172	0
SPN89	Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_ss_polymorphum_nov_97.741%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	184	0	0	111	0	0	0	266	0	0	0	0
SPP14	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp14_2	332	450	0	50	1077	109	0	0	143	375	0	1091	580	0	186	367	135	35	317	147	0	96	0	0	0	0	0	587	93	0	0	289	0	0	0	0	0	307	0	157
SPP15	Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;multispecies_spp15_2	64	231	172	30	0	172	66	73	0	0	121	107	66	17	0	70	49	31	0	0	0	0	311	95	0	0	34	0	0	0	85	0	0	357	0	0	0	0	188	0
SPP19	Bacteria;Firmicutes;Clostridia;multiorder;multifamily;Eubacterium_[XIVa][G-1];saburreum	0	53	0	75	91	45	0	56	26	0	0	0	29	83	245	0	68	0	52	79	106	39	0	103	107	109	0	0	182	279	140	0	228	0	0	0	0	0	0	144
SPP2	Bacteria;Bacteroidetes;Bacteroides;Bacteroidales;Porphyromonadaceae;Porphyromonas;multispecies_spp2_2	38	0	0	0	47	0	0	0	0	207	0	0	0	0	56	0	76	40	258	0	122	72	0	0	0	0	0	0	223	0	81	347	0	0	0	0	0	0	0	0
SPP8	Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp8_3	53	0	113	208	0	0	0	169	0	0	0	85	0	92	0	0	0	115	0	0	0	665	0	0	334	196	206	0	0	0	368	306	0	0	0	343	87	55	0	0
SPPN1	Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;goodfellowii	0	0	0	0	0	0	0	0	0	86	0	0	0	0	0	146	0	0	126	139	0	230	0	0	0	244	0	0	0	0	0	0	156	0	0	189	0	159	112	0

Download OTU Tables at Different Taxonomy Levels
Phylum	Count*:	Relative**:	CLR***:
Class	Count*:	Relative**:	CLR***:
Order	Count*:	Relative**:	CLR***:
Family	Count*:	Relative**:	CLR***:
Genus	Count*:	Relative**:	CLR***:
Species	Count*:	Relative**:	CLR***:
* Read count
** Relative abundance (count/total sample count)
*** Centered log ratio transformed abundance

;

The species listed in the table has full taxonomy and a dynamically assigned species ID specific to this report. When some reads match with the reference sequences of more than one species equally (i.e., same percent identiy and alignmnet coverage), they can't be assigned to a particular species. Instead, they are assigned to multiple species with the species notaton "s__multispecies_spp2_2". In this notation, spp2 is the dynamic ID assigned to these reads that hit multiple sequences and the "_2" at the end of the notation means there are two species in the spp2.

You can look up which species are included in the multi-species assignment, in this table below:

Another type of notation is "s__multispecies_sppn2_2", in which the "n" in the sppn2 means it's a potential novel species because all the reads in this species have < 98% idenity to any of the reference sequences. They were grouped together based on de novo OTU clustering at 98% identity cutoff. And then a representative sequence was chosed to BLASTN search against the reference database to find the closest match (but will still be < 98%). This representative sequence also matched equally to more than one species, hence the "spp" was given in the label.

Taxonomy Bar Plots for All Samples

Taxonomy Bar Plots for Individual Comparison Groups

Comparison No.	Comparison Name	Families		Genera		Species
Comparison 1	SECC vs CF	PDF	SVG	PDF	SVG	PDF	SVG

VIII. Analysis - Alpha Diversity

In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale. The term was introduced by R. H. Whittaker[1][2] together with the terms beta diversity (β-diversity) and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape (gamma diversity) is determined by two different things, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).

References:
Whittaker, R. H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338. doi:10.2307/1943563
Whittaker, R. H. (1972). Evolution and Measurement of Species Diversity. Taxon, 21, 213-251. doi:10.2307/1218190

Alpha Diversity Analysis by Rarefaction

Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found, but the curves plateau as only the rarest species remain to be sampled.

References:
Willis AD. Rarefaction, Alpha Diversity, and Statistics. Front Microbiol. 2019 Oct 23;10:2407. doi: 10.3389/fmicb.2019.02407. PMID: 31708888; PMCID: PMC6819366.

Boxplot of Alpha-diversity Indices

The two main factors taken into account when measuring diversity are richness and evenness. Richness is a measure of the number of different kinds of organisms present in a particular area. Evenness compares the similarity of the population size of each of the species present. There are many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices". Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).

Alpha Diversity Box Plots for All Groups

Alpha Diversity Box Plots for Individual Comparisons

Comparison 1

SECC vs CF

View in PDF

View in SVG

Group Significance of Alpha-diversity Indices

To test whether the alpha diversity among different comparison groups are different statisticall, we use the Kruskal Wallis H test provided the "alpha-group-significance" fucntion in the QIIME 2 diversity package. Kruskal Wallis H test is the non parametric alternative to the One Way ANOVA. Non parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks, as the ranks of the data values are used in the test rather than the actual data points. The test determines whether the medians of two or more groups are different.

Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features), 2) Shannon index, and 3) Simpson index.

Comparison 1.

SECC vs CF

Observed Features

Shannon Index

Simpson Index

IX. Analysis - Beta Diversity

NMDS and PCoA Plots

Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different groups of samples. There are many different similarity/dissimilarity metrics. In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac) or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac). They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).

For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity, which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).

MDS/PCoA is a scaling or ordination method that starts with a matrix of similarities or dissimilarities between a set of samples and aims to produce a low-dimensional graphical plot of the data in such a way that distances between points in the plot are close to original dissimilarities.

NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into the ranks and use these ranks in the calculation.

In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and NMDS separately. Below are beta diveristy results for all groups together:

NMDS and PCoA Plots for All Groups

The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR) for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called Aitchison distance.

Below are the NMDS and PCoA plots of the Aitchison distances of the samples:

NMDS and PCoA Plots for Individual Comparisons

Comparison No.	Comparison Name	NMDA				PCoA
Comparison No.	Comparison Name	Bray-Curtis		CLR Euclidean		Bray-Curtis		CLR Euclidean
Comparison 1	SECC vs CF	PDF	SVG	PDF	SVG	PDF	SVG	PDF	SVG

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

Interactive 3D PCoA Plots - Euclidean Distance

Interactive 3D PCoA Plots - Correlation Coefficients

Group Significance of Beta-diversity Indices

To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities, the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA (permutational multivariate analysis of variance) chosen s the group significan testing method.

Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance (Euclidean distance between clr-transformed compositions).

Comparison 1.

SECC vs CF

Bray–Curtis

Correlation

Aitchison

X. Analysis - Differential Abundance

16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different species in a sample, i.e., the relative abundance of species, instead of the absolute abundance. In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. This makes the microbiome read count data “compositional” (Gloor et al, 2017). In general, compositional data represent parts of a whole which only carry relative information (http://www.compositionaldata.com/).

The problem of microbiome data being compositional arises when comparing two groups of samples for identifying “differentially abundant” species. A species with the same absolute abundance between two conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion in terms of differential abundance for microbial species in the samples.

When studying differential abundance (DA), the current better approach is to transform the read count data into log ratio data. The ratios are calculated between read counts of all species in a sample to a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA species without being affected by percentage bias mentioned above

In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes) was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of features that a single feature is tested to be significantly different against. Hence the higher the "W" the more statistical sifgnificane that a feature/species is differentially abundant.

References:

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.

Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

ANCOM Differential Abundance Analysis

ANCOM Results for Individual Comparisons

Comparison No.	Comparison Name
Comparison 1.	SECC vs CF

ANCOM-BC Differential Abundance Analysis

Starting with version V1.2, we also include the results of ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that: (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement. The bias correction (BC) addresses a challenging problem of the bias introduced by differences in the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data. ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework.

References:

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

ANCOM-BC Results for Individual Comparisons

Comparison No.	Comparison Name
Comparison 1.	SECC vs CF

LEfSe - Linear Discriminant Analysis Effect Size

LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011). Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based, the differential species identified among the comparison groups is less biased (than percent abundance based).

Reference:

Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.

SECC vs CF

XI. Analysis - Heatmap Profile

Species vs Sample Abundance Heatmap for All Samples

Heatmaps for Individual Comparisons

A) Two-way clustering - clustered on both columns (Samples) and rows (organism)

Comparison No.	Comparison Name	Family Level		Genus Level		Species Level
Comparison 1	SECC vs CF	PDF	SVG	PDF	SVG	PDF	SVG

B) One-way clustering - clustered on rows (organism) only

Comparison No.	Comparison Name	Family Level		Genus Level		Species Level
Comparison 1	SECC vs CF	PDF	SVG	PDF	SVG	PDF	SVG

C) No clustering

Comparison No.	Comparison Name	Family Level		Genus Level		Species Level
Comparison 1	SECC vs CF	PDF	SVG	PDF	SVG	PDF	SVG

XII. Analysis - Network Association

To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely under-powered. Here we use SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015). SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection (GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012), which is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between the log-transformed components.

References:

Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.

Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8(9):e1002687. doi: 10.1371/journal.pcbi.1002687. Epub 2012 Sep 20. PMID: 23028285; PMCID: PMC3447976.

FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.41

Version History

Project ID: FOMC4083_4106_4511_4917

I. Project Summary

II. Workflow Checklist

III. NGS Sequencing

IV. Complete Report Download

V. Raw Sequence Data Download

VI. Analysis - DADA2 Read Processing

What is DADA2?

Analysis Procedures:

Results

Sample Meta Information

ASV Read Counts by Samples

VII. Analysis - Read Taxonomy Assignment

Read Taxonomy Assignment - Methods

The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline

Read Taxonomy Assignment - Result Summary *

Read Taxonomy Assignment - ASV Species-Level Read Counts Table

Taxonomy Bar Plots for All Samples

Taxonomy Bar Plots for Individual Comparison Groups

VIII. Analysis - Alpha Diversity

Alpha Diversity Analysis by Rarefaction

Boxplot of Alpha-diversity Indices

Group Significance of Alpha-diversity Indices

IX. Analysis - Beta Diversity

NMDS and PCoA Plots

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

Interactive 3D PCoA Plots - Euclidean Distance

Interactive 3D PCoA Plots - Correlation Coefficients

Group Significance of Beta-diversity Indices

X. Analysis - Differential Abundance

ANCOM Differential Abundance Analysis

ANCOM-BC Differential Abundance Analysis

LEfSe - Linear Discriminant Analysis Effect Size

XI. Analysis - Heatmap Profile

Species vs Sample Abundance Heatmap for All Samples

Heatmaps for Individual Comparisons

XII. Analysis - Network Association

SPIEC-EASI Network Inference by Neighborhood Selection (MB Method)

Association Network Inference by SparCC